How can GenAI be used in medical transcriptions?

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How can GenAI be used in medical transcriptions?

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Ujjwald · Answer 1 · 2024-06-22T01:31:57+0000

Step 1 - Scope the problem and ask clarifying questions

To start off, let me explain what medical transcriptions are. These are basically people (transcriptionists) taking notes from doctors/nurses on the treatment of a particular patient. These details will then get pushed to the EHR (Electronic Health Record) of the patient.

When we say GenAI here, are we specifically talking of LLMs or any other use cases which use ML and AI models? For now, I will consider a mix of both

Step 2 - List the user segments and select one

There are many users who will need transcriptions. These are doctors, nurses, patients, transcriptionists. For the purposes of this question, let's look at doctors and how we can use GenAI to help them do better transcriptions.

Step 3 - Pain Points and prioritization

Pain Point	Current Solution	Severity
No Time	Doctors rush through an appointment	S (currently this is not a problem we want to solve)
Too many patients - very difficult to go through the file of the patient	They ask the patient clarifying questions, which they may or may not know how to answer	M (Most of the times, the patient knows their file and the doctor can look for some patterns themselves)
Have to go through so much data to go through past visits - This is especially a problem if the patient is new to the doctor	If the patient is new, they need to ask a set of questions and go through the file in some detail, which is time consuming	L (This is a bigger problem when the patient sees a new doctor)
Doctor can suggest medicine that the patient is allergic to	Doctor suggests medicines but patient can have an allergic reaction	L (This is a big issue)

So, let's look to solve the pain points for the the doctor having to go through past visit history and the potential to actually suggest medication to which the patient could be allergic.

Step 4 - Solutions and their prioritization

Solution	Reach	Impact	Effort
Summarize the patient records for a quick view	L (Lot of doctors would find this useful)	L (Would be useful especially when you have so many pages for a new patient)	M (Existing foundational models should be sufficient)
Alert users/doctors for potential allergies when the medicine is recommended by the doctors	L (A lot of doctors would like to use this)	L (Sometimes allergen information is overlooked in a big file, so this will be a good value add here)	M (This will need to cross reference the compositon of the drug with allergen information from the patient. There should be existing models that do this, else new ones should be done)
Suggest new research papers the doctors can read	M (Doctors usually have their own process and may not appreciate these recommendations)	M (Don't think a lot of doctors will use this)	L (Will need to build something from the ground up based on the specializaton of the doctor)
Summarize diagnostic reports	M (Doctors have the habit of going through the entire report because they look for correlations that others might miss)	M (Doctors have the habit of going through the entire report because they look for correlations that others might miss))	M (Summarizing won't be a problem)
Generate a recommended treatment plan based on the results of the test	L (This will be good)	L (Just enter a few values and the treatment plan gets generated)	M (Once the medicines are given, and we transcribe what the doctor said, a detailed treatment plan can be generated)

Based on the above, we can use GenAI to :

Summarize the patient's past history to the doctor
Alert doctors to potential allergen information when a allergic medicine has been given
Generate a treatment plan based on the inputs of the doctor

Step 5 - Risks

The doctors still have a problem trusting technology, so we will have to work on that part.

Step 5 - Metrics

We can look at :

No. of summaries generated
No. of treatment plans generated
No. of treatment plans changed (to check accuracy of generated plans)

Knight27 · Answer 2 · 2024-06-06T05:53:39+0000

Clarification Questions

Who are the primary users of the GenAI-based medical transcription solution? --> Assuming doctors, nurses, medical transcriptionists, administrative staff
Will this solution be integrated with existing electronic health record (EHR) systems? Assuming conventional EHR
What level of accuracy is required for the transcriptions, considering the critical nature of medical documentation? --> Assuming 70% accuracy considering model accuracy
Will the AI need to handle multiple languages or medical terminologies specific to certain specialties?--> Assuming English is only language considered
What is the expected volume of transcription (e.g., number of hours of audio per day) and scale of deployment (e.g., single hospital, nationwide network)?--> Assuming only 1 metro cities so far

Potential Users

Doctors
Nurses
Medical Transcriptionist
Administrative Staff

User Flow

Audio Input: User uploads an audio file or starts a live recording.
AI Transcription: The AI processes the audio, providing a real-time or batch transcription.
Review and Edit: The user reviews the transcription, makes necessary edits, and provides feedback on accuracy.
Integration: The finalized transcription is seamlessly integrated into the EHR system.
Feedback Loop: User feedback is used to continually improve the AI model’s accuracy and performance.

Potential Solutions Using GenAI for Medical Transcriptions

1. Automated Transcription

Real-time Transcription: Develop an AI model that can transcribe medical conversations in real-time, allowing doctors to review and edit transcripts immediately.
Batch Processing: For recorded audio files, provide a service where users can upload files, and the AI processes them to generate transcriptions within minutes.

2. Enhanced Accuracy with Domain-Specific Training

Medical Vocabulary: Train the AI model specifically on medical vocabulary and terminology to improve accuracy.
Continuous Learning: Implement a feedback loop where corrections made by users are fed back into the AI model for continuous improvement.

3. Integration with EHR Systems

Seamless Workflow: Ensure that transcriptions can be directly integrated into patient records in popular EHR systems like Epic, Cerner, and Allscripts.
Contextual Understanding: Utilize AI to understand the context and categorize notes appropriately within the EHR.

4. Natural Language Processing (NLP) for Contextual Insights

Summarization: Provide AI-driven summarization of long medical dictations to highlight key points, diagnoses, and treatment plans.
Action Items: Identify and flag actionable items such as medication changes, follow-up appointments, and patient instructions.

5. Compliance and Security

Data Encryption: Ensure all audio and transcription data is encrypted both at rest and in transit.
Access Controls: Implement strict access controls and audit logs to monitor who accesses the transcription data.
HIPAA Compliance: Ensure the entire system is compliant with HIPAA regulations and other relevant standards.

6. Multilingual Support

Language Models: Develop and train models to handle multiple languages commonly used in medical practice.
Specialty-Specific Models: Create models tailored to specific medical specialties, ensuring accurate transcription of specialized terminology.

7. User Interface and Experience

Intuitive Interface: Design a user-friendly interface that allows users to easily upload audio, review transcriptions, and make edits.
Voice Commands: Incorporate voice commands to allow hands-free interaction with the system, beneficial for busy medical professionals.

8. User Feedback and Customization

Feedback Mechanism: Include a simple way for users to provide feedback on transcription accuracy and functionality.
Customization Options: Allow customization of the AI model for specific medical practices or individual preferences, such as preferred formatting and common phrases.

Summary

By leveraging GenAI for medical transcriptions, we can significantly enhance the efficiency, accuracy, and convenience of documenting medical conversations. This approach not only reduces the administrative burden on healthcare professionals but also ensures that patient records are accurate and up-to-date, ultimately improving patient care.